Time Series Forecasting

Introduction

Time series forecasting predicts future values based on historical patterns.

Simple Methods

import pandas as pd
from sklearn.metrics import mean_absolute_error

# Moving average
df["ma_7"] = df["value"].rolling(window=7).mean()
df["ma_30"] = df["value"].rolling(window=30).mean()

# Exponential smoothing
df["ema"] = df["value"].ewm(span=7).mean()

# Naive forecast
df["naive"] = df["value"].shift(1)

Prophet

from prophet import Prophet

df = pd.DataFrame({
    "ds": pd.date_range("2023-01-01", periods=365),
    "y": sales_data
})

model = Prophet(yearly_seasonality=True, weekly_seasonality=True)
model.fit(df)

future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

ARIMA

from statsmodels.tsa.arima.model import ARIMA

model = ARIMA(df["value"], order=(5, 1, 0))
fitted = model.fit()

forecast = fitted.forecast(steps=30)

Practice Problems

1. Create time series plots
2. Detect trends and seasonality
3. Use moving averages
4. Fit ARIMA model
5. Compare forecasting methods